- Open Access
- Article
Water Potability Prediction Using Neural Networks
by Ranyah Taha1,* 
, Fuad Musleh 2 and Abdel Rahman Musleh 3
1 Computer Science Dept., Al-Iman School, Bahrain
2 Civil engineering Department, College of Engineering, University of Bahrain, Sakhir, 1054, Bahrain
3 Electrical and Electronics Engineering Department, College of Engineering, University of Bahrain, Sakhir, 1054, Bahrainn
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 4, Issue 5, Page # 1-10, 2025 DOI: 10.55708/js0405001
Keywords: Artificial Intelligence, Data Analysis, Water Quality Classification, Neural Networks, Civil Engineering
Received: 13 January 2025 Revised: 02 March 2025 Accepted: 03 March 2025 Online: 20 May, 2025
(This article belongs to the Special Issue Special Issue on Multidisciplinary Sciences and Advanced Technology 2024 & Section Computer Science and Information Technology: Artificial Intelligence – Computer Science (AIC))
APA Style
Taha, R., Musleh, F., & Musleh, A. R. (2025). Water potability prediction using neural networks. Journal of Engineering Research and Sciences, 4(5), 1–10. https://doi.org/10.55708/js0405001
Chicago/Turabian Style
Taha, R., F. Musleh, and A. R. Musleh. 2025. “Water Potability Prediction Using Neural Networks.” Journal of Engineering Research and Sciences 4 (5): 1–10. https://doi.org/10.55708/js0405001.
IEEE Style
R. Taha, F. Musleh, and A. R. Musleh, “Water potability prediction using neural networks,” J. Eng. Res. Sci., vol. 4, no. 5, pp. 1–10, 2025, doi: 10.55708/js0405001.
The crucial need for maintaining specific water potability levels depending on the sector of utilization, this is becoming increasingly challenging due to the increased pollution. It is therefore important to have fast and reliable water potability assessment techniques. A subset of Machine Learning (ML); being Deep Learning (DL), can be utilized to develop models capable of measuring water quality while assessing its potability with high levels of accuracy; thus, ensuring that water meets the set standards based on the required sector of utilization. In this research, the effectiveness of Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) Neural Networks (NN) were contrasted for Water Quality Classification (WQC). The MLP model demonstrated superior performance, achieving higher precision, accuracy, F-measure, recall and the area under the receiver operating characteristic curve (ROC-AUC) scores, indicating its effectiveness in this application compared to the LSTM approach. The experimental findings revealed that MLP NN model outperformed the LSTM NN model in WQC tasks. The MLP model achieved very high performance with an accuracy of 99.9%, an F-measure of 99.9%, a precision of 99.9%, a recall of 99.9%, and a ROC-AUC of 100%, significantly outperforming the LSTM model, which attained an accuracy of 97.6%, an F-measure of 97.1%, a precision of 97.1%, a recall of 97.5%, and a ROC-AUC of 97.9%. The study’s novelty lies in employing DL for binary classification, yielding outstanding outcomes in the crucial domain of WQC.
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